Web sites of online merchants provide many types of information for assisting users in evaluating the merchants' offerings. Similarly, information commonly is collected from users to assist in generating recommendations and to assist the merchants in determining an appropriate selection and inventory. Without limiting the generality of the foregoing, such information can include: textual reviews; scaled ratings; personalized recommendations based on collaborative filtering (which may operate by identifying other users with similar tastes); content based filtered data (which relies on product descriptions to identify products similar to those purchased or highly rated by the user); various combinations of collaborative and content filtered data; and ratings based on or adjusted by age, gender, membership in “communities” selected by or identified for a user, wish lists, geographical location, educational background, occupation, annual income, and, so on. The information is helpful to users which may otherwise may not be familiar with an offering of a merchant. The information can also be valuable to a merchant for suggesting and stocking a particular offering. Sophisticated systems infer personal preference based on proprietary algorithms, whereas less sophisticated systems supply only objective information such as sales and ratings rankings, but large online merchants use, collect and provide information pertaining to both systems.
Still, there are categories of offerings for which traditional information is inaccurate or misleading, and yet which may be useful to a consumer or merchant (including content suppliers).